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Statistical reverse engineering methods for high-throughput molecular data

With the recent advancement in the high-throughput technologies, molecular biology is rapidly evolving into a quantitative science. This achievement led to an increased role of reverse engineering methods in making sense of this high-dimensional data. The development and application of such methods will guide new experiments, shed new light on existing hypotheses and will eventually trigger new discoveries that have been difficult to achieve using the traditional biochemical approaches alone. This special issue will focus on statistical reverse engineering methods for high-throughput molecular data.

Edited by: Heinz Koeppl, Maria Rodriguez Martinez and Nurgazy Sulaimanov

  1. Research

    Learning directed acyclic graphs from large-scale genomics data

    In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set...

    Fabio Nikolay, Marius Pesavento, George Kritikos and Nassos Typas

    EURASIP Journal on Bioinformatics and Systems Biology 2017 2017:10

    Published on: 20 September 2017

  2. Research

    Reverse engineering gene regulatory networks from measurement with missing values

    Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time po...

    Oyetunji E. Ogundijo, Abdulkadir Elmas and Xiaodong Wang

    EURASIP Journal on Bioinformatics and Systems Biology 2017 2017:2

    Published on: 10 January 2017

  3. Research

    Graph reconstruction using covariance-based methods

    Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the ...

    Nurgazy Sulaimanov and Heinz Koeppl

    EURASIP Journal on Bioinformatics and Systems Biology 2016 2016:19

    Published on: 23 November 2016